Is the Airbnb Vacation Rental Market a Factor In Hong Kong’s Housing Crisis?

As the world population continues to increase, housing crises are rising all over the world. The city with by far the highest degree of housing shortages is Hong Kong, China. However, Hong Kong is also the city with the highest level of tourism. As such, it becomes an interesting setting for an exploration of short-term rental prices.

Airbnb is a short-term rental company which specializes in vacation rentals. Open data can be found with detailed listings data from most major cities across the world, including Hong Kong.

For this exploration, the following will be covered:

1. General data exploration to better understand the factors at play, via data visualizations
4. Predictors of listing price
  • This is an additional machine learning exploration

The purpose is to determine potential trends in the vacation rental market in cities such as Hong Kong in which its own residents struggle to find housing.

We will begin by loading the data, which was downloaded from Inside Airbnb, which is an initiative intented to make Airbnb data open and transparent to the public, including customers and hosts alike. The data was last updated on 17 September, 2023. As listed on the Inside Airbnb website, “The data behind the Inside Airbnb site is sourced from publicly available information from the Airbnb site. The data has been analyzed, cleansed and aggregated to faciliate public discussion.” That said, the data will still require further cleaning, which can be followed below.

0. Data Set-Up

Although the data has been pre-cleaned, it requires further cleaning for the purpose of this study. To begin with, there are many variables; some of which, such as date of last data scrape, are unnecessary to the task at hand. As such, these variables have been omitted from the dataset. In addition, once the graphs have been plotted, categorical variables were then encoded as dummy variables.

1. Data Exploration - Visualizations

Price Distribution

Let us first explore the price distribution. This will give us a good estimate of the vacation rental market in Hong Kong. For reference, as found via InterNations, the average monthly rent for permanent residents of Hong Kong in the year 2023 is between 1,500 to 2,500 USD. This comes out at an average of approximately 67 USD per night.

We can see that, in fact, the average listing price per night is approximately $850. This is despite the fact that the distribution seems to be skewed to the left, in which most prices are less than $500, and many are listed at approximately $100 per night. As such, we can assume that the data is skewed by outliers. Still, the average nightly price far exceeds the average rent price for a permanent resident of Hong Kong. This suggests that high demand from tourists of wealthier countries may play a role in the housing crisis.

We will further explore this topic with a more in-depth view of the listings and hosts, as well as with a connection to census data collected by the city of Hong Kong in the year 2023.

Listing Property Types

First of all, let us consider the properties listed. In this case, there are two variables included in the dataset: one describing Property Types, and one describing Room Types. The two have shared levels, and in fact Room Types is a clustered equivalent of Property Types. In the interest of simplicity and ease of interpretation, we will use the Room Types variable in our analysis.

To begin with, we will determine the frequency of listings belonging to each Room Type, which is categorized as being either a 1. private room, 2. entire home/apt, 3. shared room, or 4. hotel room. For reference, the Property Type variable split the levels even further, including “private room in B&B,” “private room in hostel,” etc. The frequency of each of the four levels will be found using a simple vertical bar chart with frequency of each room type in the listing data.

Our findings suggest that the majority of listings can be categorized as private rooms, with the next most frequent category being entire homes or apartments. Given additional data cleanup with the use of the Property Types variable, an interesting next step would be to split the levels further and evaluate whether private rooms are more often found in permanent residents’ personal homes, in hostels, bed and breakfasts, etc. Particularly, if a large portion of private rooms are found in personal homes, then it may be a key insight into the relationship between the vacation rental market and housing crises in Hong Kong.

In addition to the Property and Room Types, a list of amenities can also be found for each listing in the dataset. Although this next analysis does not provide much insight into the topic of housing crises, let us a consider a word cloud of the most-often listed amenities.

Our findings report that the most common amenities are [air] conditioning and wifi. Another prevalent word, “allowed,” may refer to children or pets. In the dataset, we were also given columns of the listings’ descriptions and names. These columns were omitted from the analysis due to a lack of datacleanup. It is likely the data was collected using a scraping of the html on Airbnb’s website, as there are remnants of html code in the columns.

In future study, however, when such an in-depth datacleanup would be warrented, then the description would be an interesting column to explore via text analysis. This way, we may see what the most popular “attractions” are; that is, we will gain insights into what tourists value in a listing, whether that be proximity to restaurants, public transportation, etc.

5. Conclusion

We have conducted a preliminary yet in-depth analysis into the relationship between the Airbnb market and the housing situation in Hong Kong. This is an excellent first city to begin with for such analyses given Hong Kong’s high tourist levels and densly-populated, highly-demanded housing market.

Although drawing concrete conclusions would require a far more in-depth study using multiple datasets, we have derived the following hypotheses:

In general, the additional variable of profits would be extremely useful for additional analysis. Yet, in the given data, we have found that neighbourhoods and the general affluence of the hosts are important variables for consideration. Vacation rental type, which is covered by the variable room_type, as well as size, covered by accommodates (meaning how many guests a rental can accommodate) are two more obvious variables of importance, but this exploration supported the belief.

With additional data and time, it is worth exploring datasets covering income levels and general census data about the long-term and permanent residents of Hong Kong as a more in-depth point of comparison. Regardless, it is my hope that this blog post piqued your interest in the vacation rental markets and their relationship to the housing crisis in Hong Kong. A more in-depth study could easily be replicated in cities across the world, with the potential for insights for policy-making.